Can OLMo 2 32B run on NVIDIA A100 40GB?

YES — Runs Great

S88Excellent

Estimated from fit model

OLMo 2 32B needs ~28.6 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~72 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 28.6 GB, 72.3 tok/s, Runs well

28.6 GB required40.0 GB available

72% VRAM used

Fit status

Runs well

Decode

72.3 tok/s

TTFT

2679 ms

Safe context

Memory

28.6 GB / 40.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsOLMo 2 32B on NVIDIA A100 40GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 72.3 tok/s decode · 2.7s TTFT (warm) · 181 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	72.3 tok/s	1461 ms	4K
Coding	S	Runs well	72.3 tok/s	2679 ms	4K
Agentic Coding	S	Runs well	72.3 tok/s	3897 ms	4K
Reasoning	S	Runs well	72.3 tok/s	3166 ms	4K
RAG	S	Runs well	72.3 tok/s	4871 ms	4K

Quantization options

How OLMo 2 32B (32B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A78
Q3_K_S	3	15.7 GB	Low	A79
NVFP4	4

Get started

Copy-paste commands to run OLMo 2 32B on your machine.

Run

lms load OLMo-2-0325-32B-Instruct && lms server start

Your hardware

More models your NVIDIA A100 40GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.6 35B A3B	35B	S	166 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run OLMo 2 32B?

Yes, NVIDIA A100 40GB can run OLMo 2 32B with a S grade (Runs well). Expected decode speed: 72.3 tok/s.

How much VRAM does OLMo 2 32B need?

OLMo 2 32B (32B parameters) requires approximately 28.6 GB of memory with Q4_K_M quantization.

What is the best quantization for OLMo 2 32B?

The recommended quantization for OLMo 2 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will OLMo 2 32B run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, OLMo 2 32B achieves approximately 72.3 tokens per second decode speed with a time-to-first-token of 2679ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run OLMo 2 32B for coding?

For coding workloads, OLMo 2 32B on NVIDIA A100 40GB receives a S grade with 72.3 tok/s and 4K context.

What context window can OLMo 2 32B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, OLMo 2 32B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for OLMo 2 32B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/olmo-2-32b-on-a100-40gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: